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"""Utility functions specifically for NMT."""
from __future__ import print_function

import codecs
import time
import numpy as np
import tensorflow as tf

from nlp_architect.models.gnmt.utils import evaluation_utils
from nlp_architect.models.gnmt.utils import misc_utils as utils

__all__ = ["decode_and_evaluate", "get_translation"]


def decode_and_evaluate(name,
                        model,
                        sess,
                        trans_file,
                        ref_file,
                        metrics,
                        subword_option,
                        beam_width,
                        tgt_eos,
                        num_translations_per_input=1,
                        decode=True,
                        infer_mode="greedy"):
    """Decode a test set and compute a score according to the evaluation task."""
    # Decode
    if decode:
        utils.print_out("  decoding to output %s" % trans_file)

        start_time = time.time()
        num_sentences = 0
        with codecs.getwriter("utf-8")(
                tf.gfile.GFile(trans_file, mode="wb")) as trans_f:
            trans_f.write("")  # Write empty string to ensure file is created.

            if infer_mode == "greedy":
                num_translations_per_input = 1
            elif infer_mode == "beam_search":
                num_translations_per_input = min(num_translations_per_input, beam_width)

            while True:
                try:
                    nmt_outputs, _ = model.decode(sess)
                    if infer_mode != "beam_search":
                        nmt_outputs = np.expand_dims(nmt_outputs, 0)

                    batch_size = nmt_outputs.shape[1]
                    num_sentences += batch_size

                    for sent_id in range(batch_size):
                        for beam_id in range(num_translations_per_input):
                            translation = get_translation(
                                nmt_outputs[beam_id],
                                sent_id,
                                tgt_eos=tgt_eos,
                                subword_option=subword_option)
                            trans_f.write((translation + b"\n").decode("utf-8"))
                except tf.errors.OutOfRangeError:
                    utils.print_time(
                        "  done, num sentences %d, num translations per input %d" %
                        (num_sentences, num_translations_per_input), start_time)
                    break

    # Evaluation
    evaluation_scores = {}
    if ref_file and tf.gfile.Exists(trans_file):
        for metric in metrics:
            score = evaluation_utils.evaluate(
                ref_file,
                trans_file,
                metric,
                subword_option=subword_option)
            evaluation_scores[metric] = score
            utils.print_out("  %s %s: %.1f" % (metric, name, score))

    return evaluation_scores


def get_translation(nmt_outputs, sent_id, tgt_eos, subword_option):
    """Given batch decoding outputs, select a sentence and turn to text."""
    if tgt_eos:
        tgt_eos = tgt_eos.encode("utf-8")
    # Select a sentence
    output = nmt_outputs[sent_id, :].tolist()

    # If there is an eos symbol in outputs, cut them at that point.
    if tgt_eos and tgt_eos in output:
        output = output[:output.index(tgt_eos)]

    if subword_option == "bpe":  # BPE
        translation = utils.format_bpe_text(output)
    elif subword_option == "spm":  # SPM
        translation = utils.format_spm_text(output)
    else:
        translation = utils.format_text(output)

    return translation
